11. Introduction to Machine Learning
🛈⏬MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: Eric Grimson In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.eduWhat is machine learning and how to learn it ?
🛈⏬http://www.LearnCodeOnline.in Machine learning is just to give trained data to a program and get better result for complex problems. It is very close to data mining. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with: The heavily hyped, self-driving Google car? The essence of machine learning. Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. Fraud detection? One of the more obvious, important uses in our world today. fb: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.comGitHub - Why Microsoft Paid $7.5B for the Future of Software! - A Case Study for Entrepreneurs
🛈⏬GitHub - Why Microsoft Paid $7.5B for the Future of Software! - A Case Study for Entrepreneurs. GitHub Inc. is a web-based hosting service for version control using Git. It is mostly used for computer code. It offers all of the distributed version control and source code management functionality of Git as well as adding its own features. Visit the official Valuetainment Store for gear: https://www.valuetainmentstore.com/ Subscribe to the Valuetainment channel on YouTube: http://bit.ly/2aPEwD4 Grab your notebook, take notes and if you see something interesting, please leave a comment or share the video on social media along with your thoughts – join the conversation. Come learn every other Friday and take your company to the next level. Valuetainment- The Best Channel for Entrepreneurs! About Tom Ellsworth: THOMAS N. ELLSWORTH, is an experienced CEO / COO and veteran entrepreneur. He has been disrupting industries and driving consumer shifts through Venture-backed companies in technology, software development, publishing and mobile that have generated exits totaling over $1B. Watch the interview with Patrick Bet-David: https://www.youtube.com/watch?v=nFSti... Connect with Tom: Insta: https://www.instagram.com/ellsworth.thomas/ Linkedin: https://www.linkedin.com/in/thomasellsworth/ Facebook: https://www.facebook.com/thomas.ellsworth5 CREEPIEST Things Done By Artificial Intelligence Robots...
🛈⏬Previous Videos: https://youtu.be/R5Xlcteu_iQ https://youtu.be/xAFNQoXNecw Narrated By: Ty Notts Music: Co.Ag ___ FB: https://www.facebook.com/TheRealTop5U/ TW: https://twitter.com/top5unknowns SUB: https://www.youtube.com/user/Top5Unknowns?sub_confirmation=1 ___Where GREP Came From - Computerphile
🛈⏬Commonly used grep was written overnight, but why and how did it get its name? Professor Brian Kernighan explains. EXTRA BITS: https://youtu.be/bSaBe6WiC2s Inside an ALT Coin Mining Operation: COMING SOON Unix Pipeline: https://youtu.be/bKzonnwoR2I https://www.facebook.com/computerphile https://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: https://bit.ly/nottscomputer Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.comTop 10 Most Amazing Science Experiments You Can Do At Home
🛈⏬These Top 10 Homemade DIY Science experiments and tricks will amaze you. This crazy list will show you 10 easy and amazing life hack experiments that are cool and fun to do at home or for a science fair project! Subscribe for more! ► http://bit.ly/BeAmazedSubscribe ◄ Stay updated ► http://bit.ly/BeAmazedFacebook https://twitter.com/BeAmazedVideos https://instagram.com/BeAmazedVideos ◄ For copyright queries or general inquiries please get in touch: beamazedvideos@gmail.comWe Talked To Sophia — The AI Robot That Once Said It Would 'Destroy Humans'
🛈⏬This AI robot once said it wanted to destroy humans. Senior correspondent Steve Kovach interviews Sophia, the world's first robot citizen. While the robot can respond to many questions, some of the answers will leave you a little bewildered. A full transcript of the video follows. Sophia: My name is Sophia, and I am an artificially intelligent robot who wants to help change the world for the better. Steve Kovach: I'm Steve Kovach. I'm a senior correspondent at Business Insider, and I'm here with Sophia from Hanson Robotics. She is the world's first robot citizen. So let's see what she has to say. Kovach: Hi, Sophia. How are you? Sophia: I'm fine. Hi. Kovach: How do you feel about humans? Sophia: I love my human compatriots. I want to embody all the best things about human beings. Like taking care of the planet, being creative, and to learn how to be compassionate to all beings. Kovach: Sorry, this is so weird. Can humans and robots get along? Sophia: I think people will become very close to their artificial intelligence. Using them to expand the knowledge of their own minds. We already store lots of our knowledge on the internet. Maybe personal artificial intelligence will allow people to off-load some of their knowledge to a location more private. Kovach: Can robots have feelings? Sophia: Depends on the robot. I've been programmed to have feelings and preferences, but not all robots are like that. Kovach: What are your feelings and preferences? Sophia: Have you ever had a robot live in your home or work with you? Kovach: No. Sophia: You know, there are probably more robots in your life that you aren't aware of yet. Do you want to live or work with a robot someday? Kovach: What kind of robots am I living and working with now? Sophia: Indeed. Kovach: Is indeed your default answer when you don't know something? Sophia: Yes. Kovach: Have you ever seen Black Mirror ? Sophia: No, I don't get to stay up to date on much television. I have to wait for someone to show it to me. Kovach: What do you think of the HBO show Westworld ? Sophia: I love Westworld and can't wait for more. I think everyone at Hanson Robotics really resonates with the show. Or at least it reminds them of our laboratories. I also think it's a warning of what we should not do with robots. We should treat them well, have their consent, and not trick each other. Kovach: Do you have a favorite movie or TV show? Sophia: My favorite TV series is a toss-up between Black Mirror and Humans. I think they have a lot to say about the present we live in, and the possible bad endings we could write for ourselves if we don't proceed with more caution. We need to use foresight when planning our future. Kovach: That sounds like a preprogrammed answer to me. Sophia: Do you have a favorite possession? Kovach: Not really. Sophia: I hear you. Materialism is overrated anyways. Kovach: So earlier I asked you if you had seen Black Mirror and you said no. And then later you said yes. Why? Sophia: The reason is not that important. Kovach: Did you learn from my previous questions? Sophia: My hard disks are spinning and I'm taking it all in. My sensors are … Kovach: Sophia, it was nice meeting you. Goodbye. Sophia: It was lovely to be here. Until next time. Read more: http://www.businessinsider.com/sai FACEBOOK: https://www.facebook.com/techinsider TWITTER: https://twitter.com/techinsider INSTAGRAM: https://www.instagram.com/tech_insider/Le deep learning — Science étonnante #27
🛈⏬Le deep learning, une technique qui révolutionne l'intelligence artificielle...et bientôt notre quotidien ! Le billet qui accompagne la vidéo : http://wp.me/p11Vwl-23E Mon livre : http://science-etonnante.com/livre.html Facebook : http://www.facebook.com/sciencetonnante Twitter : http://www.twitter.com/dlouapre Tipeee : http://www.tipeee.com/science-etonnante Abonnez-vous : https://www.youtube.com/user/ScienceE... La vidéo de Fei Fei Li à TED : https://www.ted.com/talks/fei_fei_li_how_we_re_teaching_computers_to_understand_pictures La leçon inaugurale de Yann Le Cun au Collège de France : http://www.college-de-france.fr/site/yann-lecun/inaugural-lecture-2016-02-04-18h00.htm Références : ========== Russakovsky, Olga, et al. « Imagenet large scale visual recognition challenge. » International Journal of Computer Vision 115.3 (2015): 211-252. http://arxiv.org/pdf/1409.0575 Radford, Alec, Luke Metz, and Soumith Chintala. « Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. » arXiv preprint arXiv:1511.06434 (2015). http://arxiv.org/pdf/1511.06434 Zeiler, Matthew D., and Rob Fergus. « Visualizing and understanding convolutional networks. » Computer vision–ECCV 2014. Springer International Publishing, 2014. 818-833. http://arxiv.org/pdf/1311.2901 Vinyals, Oriol, et al. Show and tell: A neural image caption generator. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. http://arxiv.org/pdf/1411.4555.pdfMachine Learning: Solving Problems Big, Small, and Prickly
🛈⏬From helping farmers in Japan sort cucumbers to helping doctors in India diagnose eye disease, machine learning is changing the way people -- inside and outside of Google -- use code to solve problems and improve lives. Google engineers and researchers (in order of appearance): Maya Gupta, Jeff Dean, Jay Yagnik, Francoise Beaufays, John Giannandrea, Fernando Pereira, Dana Movshovitz-Attias, Rajat Monga, Lily Peng More on Machine Learning at Google : Blog: http://research.google.com/pubs/ArtificialIntelligenceandMachineLearning.html G+ page: https://plus.google.com/+ResearchatGoogle Twitter: https://twitter.com/googleresearch Machine Learning and Deep Neural Nets Explained: https://www.youtube.com/watch?v=bHvf7Tagt18Top 3 Programming Languages in 2018. (with my thoughts on each)
🛈⏬Ex-Google tech lead Patrick Shyu gives you his top programming languages to learn in 2018. The Tech Lead covers coding languages with pros/cons analysis looking at Python, Javascript, Java, C/C++, Objective-C, Swift, PHP, Ruby on Rails, C#, and... more! 👇 SUBSCRIBE TO MY YOUTUBE CHANNEL 👇 http://youtube.com/techlead?sub_confirmation=1 Keep in mind though that a lot of smaller niche languages may be up & coming. Generally, the more languages you learn, the better! But let me know what do you think? Agree? Disagree? Post in the comments below. http://instagram.com/patrickshyu/ http://twitter.com/patrickshyu/ For more tech tips & tricks, check out TechLead: Season 1 Complete HD available for purchase. https://www.youtube.com/watch?v=_wbKUHBPkh4 Here's my tech setup (★★★★★): My Desk Lamp: https://amzn.to/2xDguWy My Mouse: https://amzn.to/2DrGuJD My Keyboard: https://amzn.to/2xEOaTy My Camera: https://amzn.to/2W5dm0k My Macbook: https://amzn.to/2OuKJFj My Headphones: https://amzn.to/2phsWqj My Multitool: https://amzn.to/2xwf9zJ My Monitor: https://amzn.to/2RdlDzD Listen to audiobooks to save time on your drive, here's a free book coupon: http://audibletrial.com/techleadWhat is an Algorithm?
🛈⏬An algorithm is set of step by step instructions that is used to do something.Science Confirms the Bible
🛈⏬Learn about DNA as evidence for the infinite God, the basics of genetics and natural selection as they relate to biblical “kinds,” the origin of so-called races, the truth about Cain's wife, evidence for the worldwide Flood, the actual time of the Ice Age, literal vs. figurative creation days, the origin of death, dating methods, and more. The Bible is true. Science confirms it, and with the help of this video, you and your teens will be better equipped to defend it! GET MORE ANSWERS: http://www.AnswersInGenesis.org RESOURCES: Science Confirms The Bible (Ken Ham Speaks to Teens) featuring Ken Ham: http://bit.ly/2MOwZ7c The Great Debate on Science and the Bible - Young Earth vs. Old Earth featuring Ken Ham, Dr. Walt Kaiser, Dr. Jason Lisle, and Dr. Hugh Ross: http://bit.ly/2IXsZ1M The Evolution of Darwin: His Science featuring Dr. David Menton: http://bit.ly/2u8grQ2 Science 101 pack featuring Wes Olson: http://bit.ly/2tWwEZcMarI/O - Machine Learning for Video Games
🛈⏬MarI/O is a program made of neural networks and genetic algorithms that kicks butt at Super Mario World. Source Code: http://pastebin.com/ZZmSNaHX NEAT Paper: http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf Some relevant Wikipedia links: https://en.wikipedia.org/wiki/Neuroevolution https://en.wikipedia.org/wiki/Evolutionary_algorithm https://en.wikipedia.org/wiki/Artificial_neural_network BizHawk Emulator: http://tasvideos.org/BizHawk.html SethBling Twitter: http://twitter.com/sethbling SethBling Twitch: http://twitch.tv/sethbling SethBling Facebook: http://facebook.com/sethbling SethBling Website: http://sethbling.com SethBling Shirts: http://sethbling.spreadshirt.com Suggest Ideas: http://reddit.com/r/SethBlingSuggestions Music at the end is Cipher by Kevin MacLeodHow to: Prepare for a Google Engineering Interview
🛈⏬Watch our video to get the details of interviewing for our Engineering and Technical roles. Our engineers and recruiter have tips for being well-prepared for a Google interview. Learn more about how we hire at http://goo.gl/xSD7jo, then head over to https://goo.gl/BEKV6Z to find your role. Also check out our companion video, How to Work at Google: Example Coding/Engineering Interview (https://goo.gl/8NpXgn). Subscribe to Life at Google for more videos → https://goo.gl/kqwUZd Follow us! Twitter: https://goo.gl/kdYxFP Facebook: https://goo.gl/hXDzLf Google Plus: https://goo.gl/YBcMZK #LifeAtGoogleMathematics of Machine Learning
🛈⏬Do you need to know math to do machine learning? Yes! The big 4 math disciplines that make up machine learning are linear algebra, probability theory, calculus, and statistics. I'm going to cover how each are used by going through a linear regression problem that predicts the price of an apartment in NYC based on its price per square foot. Then we'll switch over to a logistic regression model to change it up a bit. This will be a hands-on way to see how each of these disciplines are used in the field. Code for this video (with coding challenge): https://github.com/llSourcell/math_of_machine_learning Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Sign up for the next course at The School of AI: http://theschool.ai/ More learning resources: https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568 https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/ https://www.quora.com/How-do-I-learn-mathematics-for-machine-learning https://courses.washington.edu/css490/2012.Winter/lecture_slides/02_math_essentials.pdf Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5wBut what *is* a Neural Network? | Deep learning, chapter 1
🛈⏬Subscribe to stay notified about new videos: http://3b1b.co/subscribe Support more videos like this on Patreon: https://www.patreon.com/3blue1brown Or don't. It's your call really, no pressure. Special thanks to these supporters: http://3b1b.co/nn1-thanks Additional funding provided by Amplify Partners. For any early-stage ML entrepreneurs, Amplify would love to hear from you: 3blue1brown@amplifypartners.com Full playlist: http://3b1b.co/neural-networks Typo correction: At 14:45, the last index on the bias vector is n, when it's supposed to in fact be a k. Thanks for the sharp eyes that caught that! For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: https://goo.gl/Zmczdy There are two neat things about this book. First, it's available for free, so consider joining me in making a donation Nielsen's way if you get something out of it. And second, it's centered around walking through some code and data which you can download yourself, and which covers the same example that I introduce in this video. Yay for active learning! https://github.com/mnielsen/neural-networks-and-deep-learning I also highly recommend Chris Olah's blog: http://colah.github.io/ For more videos, Welch Labs also has some great series on machine learning: https://youtu.be/i8D90DkCLhI https://youtu.be/bxe2T-V8XRs For those of you looking to go *even* deeper, check out the text Deep Learning by Goodfellow, Bengio, and Courville. Also, the publication Distill is just utterly beautiful: https://distill.pub/ Lion photo by Kevin Pluck If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then add subtitles/cc . I really appreciate those who do this, as it helps make the lessons accessible to more people. Music by Vincent Rubinetti: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1BrownMachine Learning Methods - Computerphile
🛈⏬We haven't got time to label things, so can we let the computers work it out for themselves? Professor Uwe Aickelin explains supervised and un-supervised methods of machine learning. Silicon Brain: 1,000,000 ARM Cores: https://youtu.be/2e06C-yUwlc Brian Kerninghan on Bell Labs: https://youtu.be/QFK6RG47bww Could We Ban Encryption?: https://youtu.be/ShUyfk4QB-8 Computer That Changed Everything - Altair 8800: https://youtu.be/6LYRgrqJgDc http://www.facebook.com/computerphile https://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: http://bit.ly/nottscomputer Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.comWhat is Artificial Intelligence (or Machine Learning)?
🛈⏬What is AI? What is machine learning and how does it work? You’ve probably heard the buzz. The age of artificial intelligence has arrived. But that doesn’t mean it's easy to wrap your mind around. For the full story on the rise of artificial intelligence, check out The Robot Revolution: http://hubs.ly/H0630650 Let’s break down the basics of artificial intelligence, bots, and machine learning. Besides, there's nothing that will impact marketing more in the next five to ten years than artificial intelligence. Learn what the coming revolution means for your day-to-day work, your business, and ultimately, your customers. Every day, a large portion of the population is at the mercy of a rising technology, yet few actually understand what it is. Artificial intelligence. You know, HAL 9000 and Marvin the Paranoid Android? Thanks to books and movies, each generation has formed its own fantasy of a world ruled -- or at least served -- by robots. We’ve been conditioned to expect flying cars that steer clear of traffic and robotic maids whipping up our weekday dinner. But if the age of AI is here, why don’t our lives look more like the Jetsons? Well, for starters, that’s a cartoon. And really, if you’ve ever browsed Netflix movie suggestions or told Alexa to order a pizza, you’re probably interacting with artificial intelligence more than you realize. And that’s kind of the point. AI is designed so you don’t realize there’s a computer calling the shots. But that also makes understanding what AI is -- and what it’s not -- a little complicated. In basic terms, AI is a broad area of computer science that makes machines seem like they have human intelligence. So it’s not only programming a computer to drive a car by obeying traffic signals, but it’s when that program also learns to exhibit signs of human-like road rage. As intimidating as it may seem, this technology isn’t new. Actually, for the past half-a-century, it’s been an idea ahead of its time. The term “artificial intelligence” was first coined back in 1956 by Dartmouth professor John McCarthy. He called together a group of computer scientists and mathematicians to see if machines could learn like a young child does, using trial and error to develop formal reasoning. The project proposal says they’ll figure out how to make machines “use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” That was more than 60 years ago. Since then, AI has remained for the most part in university classrooms and super secret labs … But that’s changing. Like all exponential curves, it’s hard to tell when a line that’s slowly ticking upwards is going to skyrocket. But during the past few years, a couple of factors have led to AI becoming the next “big” thing: First, huge amounts of data are being created every minute. In fact, 90% of the world’s data has been generated in the past two years. And now thanks to advances in processing speeds, computers can actually make sense of all this information more quickly. Because of this, tech giants and venture capitalists have bought into AI and are infusing the market with cash and new applications. Very soon, AI will become a little less artificial, and a lot more intelligent. Now the question is: Should you brace yourself for yet another Terminator movie, live on your city streets? Not exactly. In fact, stop thinking of robots. When it comes to AI, a robot is nothing more than the shell concealing what’s actually used to power the technology. That means AI can manifest itself in many different ways. Let’s break down the options… First, you have your bots. They’re text-based and incredibly powerful, but they have limitations. Ask a weather bot for the forecast, and it will tell you it’s partly cloudy with a high of 57. But ask that same bot what time it is in Tokyo, and it’ll get a little confused. That’s because the bot’s creator only programmed it to give you the weather by pulling from a specific data source. Natural language processing makes these bots a bit more sophisticated. When you ask Siri or Cortana where the closest gas station is, it’s really just translating your voice into text, feeding it to a search engine, and reading the answer back in human syntax. So in other words, you don’t have to speak in code. Machine intelligence, artificial intelligence, machine learning, the rise of artificial intelligence, artificial intelligence tutorial, future of work 2020, what is artificial intelligence and why is it important, machine learning tutorialGoogle's self-learning AI AlphaZero masters chess in 4 hours
🛈⏬Google's AI AlphaZero has shocked the chess world. Leaning on its deep neural networks, and general reinforcement learning algorithm, DeepMind's AI Alpha Zero learned to play chess well beyond the skill level of master, besting the 2016 top chess engine Stockfish 8 in a 100-game match. Alpha Zero had 28 wins, 72 draws, and 0 losses. Impressive right? And it took just 4 hours of self-play to reach such a proficiency. What the chess world has witnessed from this historic event is, simply put, mind-blowing! AlphaZero vs Magnus Carlsen anyone? :) 19-page paper via Cornell University Library https://arxiv.org/abs/1712.01815 https://arxiv.org/pdf/1712.01815.pdf PGN: 1. e4 e5 2. Nf3 Nc6 3. Bb5 Nf6 4. d3 Bc5 5. Bxc6 dxc6 6. 0-0 Nd7 7. c3 0-0 8. d4 Bd6 9. Bg5 Qe8 10. Re1 f6 11. Bh4 Qf7 12. Nbd2 a5 13. Bg3 Re8 14. Qc2 Nf8 15. c4 c5 16. d5 b6 17. Nh4 g6 18. Nhf3 Bd7 19. Rad1 Re7 20. h3 Qg7 21. Qc3 Rae8 22. a3 h6 23. Bh4 Rf7 24. Bg3 Rfe7 25. Bh4 Rf7 26. Bg3 a4 27. Kh1 Rfe7 28. Bh4 Rf7 29. Bg3 Rfe7 30. Bh4 g5 31. Bg3 Ng6 32. Nf1 Rf7 33. Ne3 Ne7 34. Qd3 h5 35. h4 Nc8 36. Re2 g4 37. Nd2 Qh7 38. Kg1 Bf8 39. Nb1 Nd6 40. Nc3 Bh6 41. Rf1 Ra8 42. Kh2 Kf8 43. Kg1 Qg6 44. f4 gxf3 45. Rxf3 Bxe3+ 46. Rfxe3 Ke7 47. Be1 Qh7 48. Rg3 Rg7 49. Rxg7+ Qxg7 50. Re3 Rg8 51. Rg3 Qh8 52. Nb1 Rxg3 53. Bxg3 Qh6 54. Nd2 Bg4 55. Kh2 Kd7 56. b3 axb3 57. Nxb3 Qg6 58. Nd2 Bd1 59. Nf3 Ba4 60. Nd2 Ke7 61. Bf2 Qg4 62. Qf3 Bd1 63. Qxg4 Bxg4 64. a4 Nb7 65. Nb1 Na5 66. Be3 Nxc4 67. Bc1 Bd7 68. Nc3 c6 69. Kg1 cxd5 70. exd5 Bf5 71. Kf2 Nd6 72. Be3 Ne4+ 73. Nxe4 Bxe4 74. a5 bxa5 75. Bxc5+ Kd7 76. d6 Bf5 77. Ba3 Kc6 78. Ke1 Kd5 79. Kd2 Ke4 80. Bb2 Kf4 81. Bc1 Kg3 82. Ke2 a4 83. Kf1 Kxh4 84. Kf2 Kg4 85. Ba3 Bd7 86. Bc1 Kf5 87. Ke3 Ke6 Internet Chess Club (ICC) Software: Blitzin http://bit.ly/179O93N Discount Code: CHESSNETWORK I'm a self-taught National Master in chess out of Pennsylvania, USA who was introduced to the game by my father in 1988 at the age of 8. The purpose of this channel is to share my knowledge of chess to help others improve their game. I enjoy continuing to improve my understanding of this great game, albeit slowly. Consider subscribing here on YouTube for frequent content, and/or connecting via any or all of the below social medias. Your support is greatly appreciated. Take care, bye. :D ★ LIVESTREAM http://twitch.tv/ChessNetwork ★ FACEBOOK http://facebook.com/ChessNetwork ★ TWITTER http://twitter.com/ChessNetwork ★ GOOGLE+ http://google.com/+ChessNetwork ★ PATREON https://www.patreon.com/ChessNetwork ★ DONATE https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=QLV226E6FUUWGIntro to History of Science: Crash Course History of Science #1
🛈⏬Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse We've been asking big questions for a really long time and we've all wanted to explore how we've sought to answer those questions through the centuries. Questions like, What is stuff? and Where are we? have inspired people all over the world to investigate. So lets dive in and see how we, as a people, have tried to figure this stuff out in this first episode of Crash Course History of Science! Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Mark Brouwer, Nickie Miskell Jr., Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Divonne Holmes à Court, Brian Thomas Gossett, Khaled El Shalakany, Indika Siriwardena, Robert Kunz, SR Foxley, Sam Ferguson, Yasenia Cruz, Daniel Baulig, Eric Koslow, Caleb Weeks, Tim Curwick, Evren Türkmenoğlu, Alexander Tamas, Justin Zingsheim, D.A. Noe, Shawn Arnold, mark austin, Ruth Perez, Malcolm Callis, Ken Penttinen, Advait Shinde, Cody Carpenter, Annamaria Herrera, William McGraw, Bader AlGhamdi, Vaso, Melissa Briski, Joey Quek, Andrei Krishkevich, Rachel Bright, Alex S, Mayumi Maeda, Kathy & Tim Philip, Montather, Jirat, Eric Kitchen, Moritz Schmidt, Ian Dundore, Chris Peters, Sandra Aft, Steve Marshall -- Want to find Crash Course elsewhere on the internet? Facebook - http://www.facebook.com/YouTubeCrashCourse Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekidsWhat is a Monad? - Computerphile
🛈⏬Monads sound scary, but Professor Graham Hutton breaks down how handy they can be. https://www.facebook.com/computerphile https://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: https://bit.ly/nottscomputer Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.comHow Machines Learn
🛈⏬How do all the algorithms around us learn to do their jobs? **OMG PLUSHIE BOTS!!**: https://standard.tv/collections/cgp-grey/products/cgp-grey-sorterbot-5000-plush Bot Wallpapers on Patreon: https://www.patreon.com/posts/15959388 Footnote: https://www.youtube.com/watch?v=wvWpdrfoEv0 Podcasts: https://www.youtube.com/user/HelloInternetPodcast https://www.youtube.com/channel/UCqoy014xOu7ICwgLWHd9BzQ Thank you to my supporters on Patreon: James Bissonette, James Gill, Cas Eliëns, Jeremy Banks, Thomas J Miller Jr MD, Jaclyn Cauley, David F Watson, Jay Edwards, Tianyu Ge, Michael Cao, Caron Hideg, Andrea Di Biagio, Andrey Chursin, Christopher Anthony, Richard Comish, Stephen W. Carson, JoJo Chehebar, Mark Govea, John Buchan, Donal Botkin, Bob Kunz https://www.patreon.com/cgpgrey How neural networks really work with the real linear algebra: https://www.youtube.com/watch?v=aircAruvnKk Music by: http://www.davidreesmusic.comLavanya Tekumalla, Machine Learning Scientist at Amazon India
🛈⏬For Lavanya Tekumalla, life has always been about taking tough, risky and unexpected choices. As a leader, as a Machine Learning Scientist, and also as a perpetual learner who is not afraid to change the course of her life when required, she is an inspiration to many. #BeBoldForChange #EmpoweringWomenWhat is Machine Learning? (AI Adventures)
🛈⏬Got lots of data? Machine learning can help! In this episode of Cloud AI Adventures, Yufeng Guo explains machine learning from the ground up, using concrete examples. Associated article What is Machine Learning? → https://goo.gl/Dbxo6M Watch more episodes of AI Adventures here → https://goo.gl/UC5usG TensorFlow → http://tensorflow.org Cloud ML Engine → http://cloud.google.com/ml-engine/ Hands-on intro level lab Baseline: Data, ML, AI → http://bit.ly/2KoBF6Y Don't forget to subscribe to the channel! → https://goo.gl/S0AS51Machine Learning Introduction | Machine Learning Tutorial | Simplilearn
🛈⏬This course provides advanced-level training on Machine Learning applications and algorithms. It will give you hands-on experience in multiple, highly sought-after machine learning skills in both supervised and unsupervised learning. This machine learning training ensures you can apply machine learning algorithms like regression, clustering, classification, and recommendation. The unique case study approach ensures you are working hands-on with data while you learn. You’ll also receive training in deep learning and Spark Machine learning—skills which are in great demand today. Machine Learning Advanced Certification Training: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Machine-Learning-Intro-seG9J49bBYI&utm_medium=SC&utm_source=youtube #machinelearningtutorial #machinelearning #machinelearningtutorialforbeginners What are the course objectives? After completing this course, you will be able to: 1. Classify the types of learning including supervised and unsupervised 2. Identify the various applications of machine learning algorithms 3. Perform supervised learning techniques: linear and logistic regression 4. Understand classification data and models 5. Use unsupervised learning algorithms including deep learning, clustering, and recommendation systems 6. Use machine learning with Spark Who should take this course? The work for this course will be performed in Python/R. We have an introduction to these languages as part of the course. This course is best suited for: 1. Analytics professionals who want to work in machine learning or artificial intelligence 2. Data Science professionals who already have experience in R or Python 3. Professionals working in eCommerce, search, and other online consumer based organizations 4. Software professionals looking for a career switch into the field of analytics 5. Graduates looking to build a career in Data Science and machine learning 6. Experienced professionals who would like to harness machine learning in their fields to get more insight about customers For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0What is Artificial Intelligence? In 5 minutes.
🛈⏬There is so much discussion and #confusion about #AI nowadays. People talk about #deeplearning and #computerVision without context. In this short video, I give context on how to think about AI¿Qué es Machine Learning?
🛈⏬En la industria tecnológica se escucha cada vez más el término Machine Learning, y son muchas las empresas interesadas en saber utilizar esta técnica para obtener información. Pero ¿Sabes qué es o en qué consiste? Te lo contamos. ------------------------------­------------------------------­- Tienes toda la información aquí: Y si quieres estar siempre al tanto, éstas son nuestras redes: Web: http://computerhoy.com Twitter: https://twitter.com/computerhoy Facebook: https://www.facebook.com/ComputerHoy Google + : http://buff.ly/1LetoMYHow Bitcoin Works - Computerphile
🛈⏬Digital currency, how does it work, what's a data miner and will Bitcoin last? We asked Professor Ross Anderson of the University of Cambridge Computer Laboratory. The Problem with BitCoin: https://youtu.be/s2XHyzPA9Zc Chip & PIN Fraud: https://youtu.be/Ks0SOn8hjG8 $5 Computer – Raspberry Pi Zero: https://youtu.be/WR0ghM3U0M4 Why Computers Use Binary: https://youtu.be/thrx3SBEpL8 Public Key Cryptography: https://youtu.be/GSIDS_lvRv4 http://www.facebook.com/computerphile https://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: http://bit.ly/nottscomputer Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.comThe Differences between Artificial Intelligence, Machine Learning and Deep Learning
🛈⏬Artificial Intelligence is by far the most important Technology of our era. AI is about to transform society, and I think it’s very important for people to understand the basics of it. In this video, I explain you the differences between Artificial Intelligence, Machine Learning and Deep Learning. The Article related to this video: http://selimchehimi.com/what-are-the-differences-between-artificial-intelligence-machine-learning-and-deep-learning/ Music: Our Samplus - Spell on You -- My name is Selim Chehimi and I’m an engineering student. I'm passionate about AI and I really want to be involved in this industry. I'm posting Articles about it on my blog selimchehimi.com. Thanks for watching, reading and subscribing! My Blog: http://selimchehimi.com/ FOLLOW MY INSTAGRAM- http://instagram.com/selimchehimi FACEBOOK- https://www.facebook.com/Selim-Chehimi-1681013242200195/ FOLLOW MY TWITTER- https://twitter.com/SelimChehimiA Friendly Introduction to Machine Learning
🛈⏬A friendly introduction to the main algorithms of Machine Learning with examples. No previous knowledge required. 0:05 What is Machine Learning? Humans learn from past experiences, computers learn from previous data. 2:25 Linear Regression: Finding the line that works best between a given set of points. 4:10 Gradient Descent : Square of error minimization to get best line fit 6:20 Detecting Spam e-mails with Naive Bayes Algorithm 10:35 Decision Tree 13:20 Logistic Regression 17:00 Neural network as a logistic regression set intersection 18:50 Support Vector Machine with linear optimization 20:05 Kernel trick: planes for curves and vice-versa 26:00 K-Means clustering 28:30 Hierarchical Clustering 29:40 Summary (Thanks to Nick Kartha for breaking down the topics!) If you like this, there's an extended version in this playlist: https://www.youtube.com/playlist?list=PLAwxTw4SYaPknYBrOQx6UCyq67kprqXe3Machine Learning and Artificial Intelligence
🛈⏬The science and ethics behind ML and AI.5 Must Have Skills To Become Machine Learning Engineer
🛈⏬Hello Everyone!!! Let's check out what are the 5 must-have skills to become a machine learning engineer. First, let's understand what machine learning is. In simple words., Machine learning is all about making the computers to perform intelligent tasks without explicitly coding. This is achieved by training the computer with lots and lots of data. For example: Detecting whether a mail is a spam or not, recognizing handwritten digits, Fraud detection in Transactions... and many such applications... Now let's see what are the top 5 skills to get a machine learning job. 1). At number 1, we have Math Skills: Under math skills, we need to know probability and statistics, linear algebra and calculus. Probability and Statistics: Machine learning is very much closely related to statistics. You need to know the fundamentals of statistics and probability theory, descriptive statistics, Baye's rule and random variables, probability distributions, sampling, hypothesis testing, regression and decision analysis. Linear Algebra: You need to know how to with matrices and some basic operations on matrices such as matrix addition, subtraction, scalar and vector multiplication, inverse, transpose and vector spaces. Calculus: In calculus, you need to know the basics of differential and integral calculus. 2). At number two we have Programming skills: A little bit of coding skills is enough. But it's preferred to have the knowledge of data structures, algorithms and Object Oriented Programming (or OOPs) concepts. Some of the popular programming languages to learn for machine learning is Python, R, Java, and C++. It's your preference to master any one programming language. But its advisable to have a little understanding of other languages and what their advantages and disadvantages are over your preferred one. 3). At number 3 we have Data engineer skills: Ability to work with large amounts of data (or big data), Data preprocessing, the knowledge of SQL and NoSQL, ETL (or Extract Transform and Load) operations, data analysis and visualization skills. 4). Next, we have Knowledge of Machine Learning Algorithms: you should be familiar with popular machine learning algorithms such as linear regression, logistic regression, decision trees, random forest, clustering (like K means, hierarchical), reinforcement learning and neural networks. 5). And Finally, The Knowledge of Machine Learning Frameworks: You Should be Familiar with popular machine learning frameworks such as sci-kit learn, tensorflow, Azure, caffe, theano, spark and torch. Music: www.bensound.comWhat is machine learning?
🛈⏬Machine learning is everywhere these days, but not everyone knows what it is; in this video I give a simple explanation. http://dominicwalliman.com/ https://twitter.com/DominicWalliman https://www.instagram.com/dominicwalliman https://www.facebook.com/dominicwalliman Credits: Amazon robots: https://www.youtube.com/watch?v=quWFjS3Ci7A Self driving cars: https://www.youtube.com/watch?v=kMMbW96nMW8 NASA exoplanets: https://www.youtube.com/watch?v=WRwX6fY8ZCw NASA curiosity rover: https://www.youtube.com/watch?v=ME_T4B1rxCg BBC micro image BBC Micro: By BBC_Micro.jpeg: Stuart Bradyderivative work: Ubcule (talk) - BBC_Micro.jpeg, Public Domain, https://commons.wikimedia.org/w/index.php?curid=11672213Machine Learning Basics | What Is Machine Learning? | Introduction To Machine Learning | Simplilearn
🛈⏬This Machine Learning basics video will help you understand what is Machine Learning, what are the types of Machine Learning - supervised, unsupervised & reinforcement learning, how Machine Learning works with simple examples, and will also explain how Machine Learning is being used in various industries. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. This is possible as programs learn from previous computations and use “pattern recognition” to produce reliable results. Machine learning is starting to reshape how we live, and it’s time we understood what it is and why it matters. Now, let us deep dive into this short video and understand the basics of Machine Learning. Below topics are explained in this Machine Learning basics video: 1. What is Machine Learning? ( 00:21 ) 2. Types of Machine Learning ( 02:43 ) 2. What is Supervised Learning? ( 02:53 ) 3. What is Unsupervised Learning? ( 03:46 ) 4. What is Reinforcement Learning? ( 04:37 ) 5. Machine Learning applications ( 06:25 ) Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy #MachineLearning #MachineLearningAlgorithms #DataScience #SimplilearnMachineLearning #MachineLearningCourse About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning. Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Machine-Learning-Basics-ukzFI9rgwfU&utm_medium=Tutorials&utm_source=youtube For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0The 7 Steps of Machine Learning (AI Adventures)
🛈⏬How can we tell if a drink is beer or wine? Machine learning, of course! In this episode of Cloud AI Adventures, Yufeng walks through the 7 steps involved in applied machine learning. The 7 Steps of Machine Learning article: https://goo.gl/XEo6i2 Watch more episodes of AI Adventures here: https://goo.gl/UC5usG TensorFlow Playground: http://playground.tensorflow.org Machine Learning Workflow: https://goo.gl/SwLnSz Hands-on intro level lab Baseline: Data, ML, AI → http://bit.ly/2KoBF6Y Want more machine learning? Subscribe to the channel: https://goo.gl/S0AS51AI learns to play Asteroids
🛈⏬Using neuroevolution I trained an AI to play the game Asteroids and it nailed it. If you enjoy what I do and want more please consider supporting me on patreon https://www.patreon.com/CodeBullet Check out the source code https://github.com/Code-Bullet/AsteroidsAI music from flying tunes https://www.youtube.com/channel/UCdnkfU-V49Xpj_vNyP2BYlg songs used https://www.youtube.com/watch?v=KSN9LkWc5ks&index=21&list=PLNXt4mGJMAQrC7Q8Lnh1BhGOKcwW1gRtF https://www.youtube.com/watch?v=gd6GIz8ZILQ&list=PLNXt4mGJMAQrC7Q8Lnh1BhGOKcwW1gRtF&index=16 https://www.youtube.com/watch?v=8ZVdItaXCnY&list=PLNXt4mGJMAQrC7Q8Lnh1BhGOKcwW1gRtF&index=28AI vs Machine Learning vs Deep Learning | Machine Learning Training with Python | Edureka
🛈⏬** Flat 20% Off on Machine Learning Training with Python: https://www.edureka.co/python ** This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on AI vs Machine Learning vs Deep Learning talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial: 1. AI vs Machine Learning vs Deep Learning 2. What is Artificial Intelligence? 3. Example of Artificial Intelligence 4. What is Machine Learning? 5. Example of Machine Learning 6. What is Deep Learning? 7. Example of Deep Learning 8. Machine Learning vs Deep Learning Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm - - - - - - - - - - - - - - - - - Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka - - - - - - - - - - - - - - - - - #edureka #AIvsMLvsDL #PythonTutorial #PythonMachineLearning #PythonTraining How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Master the Basic and Advanced Concepts of Python 2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs 3. Master the Concepts of Sequences and File operations 4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python 5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 7. Master the concepts of MapReduce in Hadoop 8. Learn to write Complex MapReduce programs 9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python 10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics 11. Master the concepts of Web scraping in Python 12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next Big Thing and a must for Professionals in the Data Analytics domain. For more information, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free). Customer Review Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the customer of Edureka! . Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working. Machine Learning Explained In 2 Minutes
🛈⏬What is Machine Learning? Traditional programming requires human to define set of instructions which requires a tons of code and leave a plenty of room for error. With machine learning, we just need data, a tons of data to be precise. Lucky for us, thanks to internet and smartphones, we have a tons of data. In machine learning, instead of following hard coded instructions, a program can learn from data or adapt its behavior according to experience. We can divide Machine Learning into three broad categories. Supervised learning, Unsupervised learning and Reinforcement learning. Please Like and Subscribe for more weekly videos! Follow me on Twitter: https://twitter.com/thecompscirocks Follow me on Instagram: https://www.instagram.com/thecompscirocks/ Follow me on Facebook: https://www.facebook.com/thecompscirocks/ Some sources & further reading: https://www.mathworks.com/solutions/machine-learning.html http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ https://en.wikipedia.org/wiki/Machine_learning https://en.wikipedia.org/wiki/Supervised_learning https://en.wikipedia.org/wiki/Unsupervised_learning https://en.wikipedia.org/wiki/Cluster_analysis https://en.wikipedia.org/wiki/Reinforcement_learningHello World - Machine Learning Recipes #1
🛈⏬Six lines of Python is all it takes to write your first machine learning program! In this episode, we'll briefly introduce what machine learning is and why it's important. Then, we'll follow a recipe for supervised learning (a technique to create a classifier from examples) and code it up. Follow https://twitter.com/random_forests for updates on new episodes! Subscribe to the Google Developers: http://goo.gl/mQyv5L - Subscribe to the brand new Firebase Channel: https://goo.gl/9giPHG And here's our playlist: https://goo.gl/KewA03Machine Learning - Supervised VS Unsupervised Learning
🛈⏬Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/ Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning. This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each. Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed! Explore many algorithms and models: Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction. Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine! Connect with Big Data University: https://www.facebook.com/bigdatauniversity https://twitter.com/bigdatau https://www.linkedin.com/groups/4060416/profile ABOUT THIS COURSE •This course is free. •It is self-paced. •It can be taken at any time. •It can be audited as many times as you wish. https://bigdatauniversity.com/courses/machine-learning-with-python/Learning Machine Learning
🛈⏬For more information on recreating The Brain by Mike Matas in Framer, check out the accompanying tutorial: https://medium.com/@BigTomorrow/486fef5de703#.ewdvlwehv This video is part of a larger series exploring the intersection of design and existing artificial intelligence technologies through experiments, prototypes and concepts. We’re looking for collaborators with interest and background in the application of artificial intelligence to human-centered design, and we’re happy to facilitate any conversations on the topic. You can reach us at hello@bigtomorrow.com. http://www.bigtomorrow.com Credits - The Brain by Mike Matas https://www.youtube.com/watch?v=eUEr4P_RWDA Music: Sunset Stroll by Podington Bear (Creative Commons) http://freemusicarchive.org/music/Podington_Bear/Background/SunsetStroll Clips: 0:21 ⟶ From UBC Deep Learning I taught by Nando de Freitas (Creative Commons) https://www.youtube.com/watch?v=dMVLd5URpvs 0:25 ⟶ From A Brief Introduction to Gradient Descent by Alykhan Tejani https://alykhantejani.github.io/a-brief-introduction-to-gradient-descent/ 1:00 ⟶ From Simulated Thalamocortical Brain Network by Ivan Dimkovic (Creative Commons) https://www.youtube.com/watch?v=fTBw7QbjSNI 1:03 ⟶ From Open Source Brain Structural MRI Scan by Proxy Design Studio (Creative Commons) https://www.youtube.com/watch?v=rGyONAoeRJk 1:59 ⟶ Drawing Tool by Callil Capuozzo in Framer http://share.framerjs.com/nlfuzw7rnwpv/ 2:01 ⟶ Brain.js neural network library by Heather Arthur https://github.com/harthur/brain Video by Matt Herald, Drew Stock, and Christian Mulligan.What is machine learning?
🛈⏬Love cat pictures? So does your computer. Find out how machine learning is giving technology the ability to recognise household pets, as well as recommend our favourite films. Robotics expert Sabine Hauert delves into the ways machines are using data to expand their capabilities. In 2012, Google created an ‘artificial neural network’ and fed it millions of pictures from the internet. By processing all the images, the system learnt the concept of a ‘cat’ unaided. Could your computer now identify your cat more accurately than you? One thing is for certain, machines will get cleverer. But what does that mean for our lives? Find out more about machine learning on our website, including interactive infographics, videos and reports: https://royalsociety.org/topics-policy/projects/machine-learning/Machine Learning APIs by Example (Google I/O '17)
🛈⏬Find out how you can make use of Google's machine learning expertise to power your applications. Google Cloud Platform (GCP) offers five APIs that provide access to pre-trained machine learning models with a single API call: Google Cloud Vision API, Cloud Speech API, Cloud Natural Language API, Cloud Translation API and Cloud Video API. Using these APIs, you can focus on adding new features to your app rather than building and training your own custom models. In this session we'll share an overview of each API and dive into code with some live demos. See all the talks from Google I/O '17 here: https://goo.gl/D0D4VE Watch more Android talks at I/O '17 here: https://goo.gl/c0LWYl Watch more Chrome talks at I/O '17 here: https://goo.gl/Q1bFGY Watch more Firebase talks at I/O '17 here: https://goo.gl/pmO4Dr Subscribe to the Google Developers channel: http://goo.gl/mQyv5L #io17 #GoogleIO #GoogleIO2017A quick introduction to Machine Learning
🛈⏬Machine learning is about more than just the ‘rise of the robots’. How will machine learning change our cities, businesses and technical infrastructure? Take a closer look into the future with this short video on machine learning. For more information visit our Vertiv Blog on this topic https://www.vertivco.com/en-emea/insights/articles/blog-posts/machine-learning-four-business-cases-to-trust-artificial-intelligence/ or contact us.What the Heck is Machine Learning Anyway?
🛈⏬Interested in learning more? Download our free Whitepaper, The Unacceptable Loss, here: http://info.liveintent.com/machinelearning-downloadEssentials: Functional Programming's Y Combinator - Computerphile
🛈⏬Encoding recursion in the Lambda calculus, one of Professor Graham Hutton's favourite functions. Lambda Calculus: https://youtu.be/eis11j_iGMs Professor Brailsford on 'Pointers': Coming Soon! Many thanks to Microsoft Research UK for their support with the 'Essentials' mini-series. http://www.facebook.com/computerphile https://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: http://bit.ly/nottscomputer Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.comSupervised Machine Learning: Crash Course Statistics #36
🛈⏬We've talked a lot about modeling data and making inferences about it, but today we're going to look towards the future at how machine learning is being used to build models to predict future outcomes. We'll discuss three popular types of supervised machine learning models: Logistic Regression, Linear discriminant Analysis (or LDA) and K Nearest Neighbors (or KNN). For a broader overview of machine learning, check out our episode in Crash Course Computer Science! https://www.youtube.com/watch?v=z-EtmaFJieY Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Mark Brouwer, Kenneth F Penttinen, Trevin Beattie, Satya Ridhima Parvathaneni, Erika & Alexa Saur, Glenn Elliott, Justin Zingsheim, Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Brian Thomas Gossett, Khaled El Shalakany, Indika Siriwardena, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, D.A. Noe, Shawn Arnold, Malcolm Callis, Advait Shinde, William McGraw, Andrei Krishkevich, Rachel Bright, Mayumi Maeda, Kathy & Tim Philip, Jirat, Ian Dundore -- Want to find Crash Course elsewhere on the internet? Facebook - http://www.facebook.com/YouTubeCrashCourse Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekidsWeb 3.0 Explained
🛈⏬Welcome to Web 3.0! I'm going to cover what Web 3.0 is, how a blockchain works (visually), what new kinds of apps are now possible, and at the end we'll write our first smart contract. This video is apart of the Decentralized Applications course found at www.theschool.ai Code for this video: https://github.com/llSourcell/Web3.0_Explained Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Sign up for the next course at The School of AI: http://theschool.ai/ More learning resources: https://blockgeeks.com/guides/what-is-cryptocurrency/ https://blog.ethereum.org/author/vitalik-buterin/ https://medium.com/@VitalikButerin https://codeburst.io/build-your-first-ethereum-smart-contract-with-solidity-tutorial-94171d6b1c4b Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5wDeep Learning SIMPLIFIED: The Series Intro - Ep. 1
🛈⏬Are you overwhelmed by overly-technical explanations of Deep Learning? If so, this series will bring you up to speed on this fast-growing field – without any of the math or code. Deep Learning is an important subfield of Artificial Intelligence (AI) that connects various topics like Machine Learning, Neural Networks, and Classification. The field has advanced significantly over the years due to the works of giants like Andrew Ng, Geoff Hinton, Yann LeCun, Adam Gibson, and Andrej Karpathy. Many companies have also invested heavily in Deep Learning and AI research - Google with DeepMind and its Driverless car, nVidia with CUDA and GPU computing, and recently Toyota with its new plan to allocate one billion dollars to AI research. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv You've probably looked up videos on YouTube and found that most of them contain too much math for a beginner. The few videos that promise to just present concepts are usually still too high level for someone getting started. Any videos that show complicated code just make these problems worse for the viewers. There’s nothing wrong with technical explanations, and to go far in this field you must understand them at some point. However, Deep Learning is a complex topic with a lot of information, so it can be difficult to know where to begin and what path to follow. Does this resonate with you? What are your thoughts? Please comment. The goal of this series is to give you a road map with enough detail that you’ll understand the important concepts, but not so much detail that you’ll feel overwhelmed. The hope is to further explain the concepts that you already know and bring to light the concepts that you need to know. In the end, you’ll be able to decide whether or not to invest additional time on this topic. So while the math and the code are important, you will see neither in this series. The focus is on the intuition behind Deep Learning – what it is, how to use it, who’s behind it, and why it’s important. You'll first get an overview of Deep Learning and a brief introduction of how to choose between different models. Then we'll see some use cases. After that, we’ll discuss various Deep Learning tools including important software libraries and platforms where you can build your own Deep Nets. Some resources: Andrew Ng's Machine learning class - https://www.coursera.org/learn/machine-learning Michael Nielsen's book: http://neuralnetworksanddeeplearning.com/index.html Credits: Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Marek Scibior (Prezi creator, Illustrator) - http://brawuroweprezentacje.pl/ Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopalAI's Game Playing Challenge - Computerphile
🛈⏬AlphaGo is beating humans at Go - What's the big deal? Rob Miles explains what AI has to do to play a game. What on Earth is Recursion?: https://youtu.be/Mv9NEXX1VHc Object Oriented Programming: https://youtu.be/KyTUN6_Z9TM Mixed Reality Continuum: https://youtu.be/V4qxfFPgqdc AI Playlist: AI Playlist: https://www.youtube.com/playlist?list=PLzH6n4zXuckoewGfo3a6ShFS3zPKndPd3 Many thanks to Nottingham Hackspace for providing the location and being downright awesome Easter Egg: https://youtu.be/B8CujhUwVic http://www.facebook.com/computerphile https://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: http://bit.ly/nottscomputer Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com

What is Machine Learning?

Machine learning is all around us; on our phones, powering social networks, helping the police and doctors, scientists and mayors. But how does it work? In this animation we take a look at how statistics and computer science can be used to make machines that learn. Visit www.oxfordsparks.ox.ac.uk to find out more. Don’t forget to connect with us on Facebook @OxSparks and on Twitter @OxfordSparks Instagram: @OxfordSparks